The present invention relates to an echo canceller for cancelling an echo signal which causes howling and constitutes an impediment to hearing in a 2-wire/4-wire conversion system and a hands-free telecommunication system.
With the spread of satellite communications and audio teleconferences, there has been a strong demand for telecommunication equipment which possesses excellent simultaneous conversation performance and suppresses echoes well. Echo cancellers have been proposed to meet such requirements. FIG. 1 is a block diagram showing a conventional echo canceller disclosed in Japanese Patent Application Laid Open No. 220530/89, the echo canceller being shown to be applied to hands-free telecommunication. In a telecommunication system including a receiving system from a receive input end 1 to a loud speaker 2 for receiving an input signal x(t) from a microphone (not shown) disposed at a different site and a sending system from a microphone 3 via a send out end 4 to a loud speaker (not shown) disposed at the above-mentioned different site, the received input signal x(t) is sampled by an analog-to-digital (hereinafter referred to simply as A/D) converter 8 and the sampled received input signal x(n) is supplied to an estimated echo path 7 from the loud speaker 2 to the microphone 3 which approximates an echo path Pe of an impulse response h(t). On the other hand, an echo signal y(t), which has reached the microphone 3 via the echo path Pe from the loud speaker 2, is sampled by an A/D converter 5 into an echo signal y(n) and an echo replica signal y(n) from the estimated echo path 7 is subtracted by a subtractor 9 from the echo signal y(n) to cancel the latter.
It is necessary that the estimated echo path 7 follow up temporal variations of the echo path Pe. In the above-said example the estimated echo path 7 is formed by a digital finite impulse response (hereinafter referred to simply as FIR) filter and its filter coefficients are iteratively adjusted by a coefficient calculation part 6 using, for example, a least mean square algorithm (hereinafter referred to simply as LMS algorithm), normalized LMS algorithm, or affine projection algorithm so that the residual echo e(n)=y(n)-y(n) may approach zero. By such adjustment of the estimated echo path 7, optimum echo cancellation is maintained at all times.
FIG. 2 shows, by way of example, the internal construction of the coefficient calculation part 6 which employs an exponentially weighted step size algorithm (hereinafter referred to simply as ES algorithm) set forth in the above-mentioned Japanese Patent Application or a literature, S. Makino et al., "Acoustic Echo Canceller Algorithm based on the Variation Characteristics of a Room Impulse Response," IEEE 1990 International Conference on Acoustics, Speech, and Signal Processing, pp. 1133-1136. Received input signals x(n) are sequentially written into a received input signal storage 14, the contents of which are updated so that it always holds the same number of the latest sample values as the number of taps of the FIR filter. All the received input signals held in the storage 14 are handled as a vector x(n) and .dbd.x(n).dbd..sup.2 is calculated in a norm calculator 13. In a step size matrix storage 12 is stored a step size matrix A which is a diagonal matrix. In the case where the estimated echo path 7 is formed by the above-mentioned digital FIR filter, its filter coefficients n(n) are a direct simulation of a room impulse response h(t). Consequently, the values of adjustment of the filter coefficients which are required in accordance with the variation of the echo path Pe coincide with the amount of variation of the room impulse response h(t). Then, the step size matrix A, which represents the step in the filter coefficient adjusting operation, is weighted in terms of a temporal variation characteristic of the impulse response h(t). In general, the impulse response in a room sound field undergoes an exponential attenuation, and also when the impulse response varies in response to a movement of an object in the sound field, the impulse response is exponentially attenuated and the difference between its values before and after the variation also has an exponential attenuation characteristic. That is, the amount of variation of the impulse response can be represented as an exponential function using an attenuation ratio .gamma.. Accordingly, the value of each of the diagonal elements (hereinafter referred to simply as a step size) .alpha..sub.i (i=1, 2, . . . , L, where L is the filter order) of the step size matrix A is chosen so that as i increases, it exponentially attenuates from .alpha..sub.max with the same slope as that of the exponential attenuation characteristic of the impulse response and gradually approaches .alpha..sub.min as shown in FIG. 3. The signals x(n), .dbd.x(n).dbd..sup.2 and e(n) and the step size matrix A are provided to an adjusting value calculation part 15 to calculate ##EQU1## The calculated output is applied to an adder 16, wherein it is added to the filter coefficients n(n) from a filter coefficient storage 11 to obtain n(n+1). The added output n(n+1) is provided to the estimated echo path 7 and, at the same time, it is supplied to the filter coefficient storage 11 to update its stored values. By such operations as mentioned above, the impulse response n(n) of the estimated echo path 7 is iteratively adjusted following Eq. (2) to approach the impulse response h(t) of the true echo path Pe. In the above, A=diag[.alpha..sub.1, .alpha..sub.2, . . . , .alpha..sub.L ], the step size matrix; .alpha..sub.i =.alpha..sub.0 .gamma..sup.i-1 (i=1, 2, . . . , L); .gamma. is the exponential attenuation ratio of the amount of variation of the impulse response; L is the filter order; n(n)=(n.sub.1 (n), n.sub.2 (n), . . . , n.sub.L (n)).sup.T, the impulse response of the estimated echo path, i.e. coefficients of the FIR filter; e(n) is an estimated error (=y(n)-y(n)), i.e. the residual echo; x(n)=(x(n), x(n-1), . . . , x(n-L+1)).sup.T, the vector of the received input signal; and T is the transpose of the vector. A necessary and sufficient condition for the convergence of Eq. (2) with white noise is that the mean .alpha. of L step sizes .alpha..sub.i (i=1, 2, . . . , L) is between 0 and 2 as expressed by the following equation (3): ##EQU2## For a speech signal a sufficient condition of the following equation (4) is used. EQU 0&lt;.alpha..sub.i &lt;2(i=1, 2, . . . , L) (4)
The mean value .alpha. influences the convergence rate; namely, when the mean value .alpha. is 1, the convergence rate is maximum and as the mean value becomes smaller than 1, the convergence rate decreases. Further, the mean value .alpha. defines the final steady-state echo return loss enhancement (ERLE) as expressed below by the following equation (5): ##EQU3## Where SNR is the SN ratio between the echo signal y(n) in the microphone 3 and ambient noise. From Eq. (5) it can be seen that the steady-state ERLE increases as the mean value .alpha. decreases.
This algorithm utilizes an acoustic finding that when the impulse response varies with a movement of a man or object, the amount of variation (i.e. the difference in the impulse response) exponentially attenuates with the same attenuation ratio as that of the impulse response. By adjusting coefficients in large steps at an early stage of the impulse response of a great change and adjusting coefficients in small steps at a latter stage of the impulse response of a slight change, it is possible to offer an echo canceller of high convergence speed.
In conventional algorithms the step size matrix is weighted by the variation characteristic of the impulse response over the full band. However, this poses a problem that the convergence rate decreases under some conditions of the echo path.
On the other hand, there has been proposed by S. Gay et al. such a system as shown in FIG. 4, in which the received input signal x(t) is analyzed or divided by a subband analysis circuit 17 into a plurality of subbands where frequency components can be regarded substantially flat; the aforementioned estimated echo path 7.sub.k and coefficient calculation part 6.sub.k for calculating its impulse response n(n) are provided for each subband; the echo signal y(t) is similarly analyzed or divided by a subband analysis circuit 18 into the above-mentioned subbands; the output echo replica signal from the estimated echo path 7.sub.k is subtracted from the divided echo signal by the aforementioned subtractor 9.sub.k for each corresponding subband to obtain the residual echo signal; the coefficients of the filter forming the estimated echo path 7.sub.k are adjusted by the coefficient calculation part 6.sub.k so that the residual echo signal may be minimized; and the residual echo signals in the respective subbands are combined or synthesized by a subband synthesis circuit 19 into a composite residual echo signal e(t) of the full band (S. Gay et al., "Fast Converging Subband Acoustic Echo Cancellation Using RAP on the WE DSP 16A," IEEE 1990 International Conference on Acoustics, Speech, and Signal Processing, pp. 1141-1144). In this case, however, each coefficient calculation part 6.sub.k does not use the step size matrix A for the calculation of Eq. (2) but instead uses a constant .alpha. (scalar) common to all the subbands. This method reduces the amount of computation for echo cancellation, by dividing the received input signal into a plurality of subbands and increases the convergence speed of the impulse response n(n) according to the iterated calculation of Eq. (2) by whitening the received input signal analyzed into each subband, but it cannot be said that a satisfactory convergence speed has been achieved.